We evaluate our distribution calibration strategy on miniImageNet ([1]}), tieredImageNet ([2]}) and CUB ([3]}).
miniImageNet and tieredImageNet have a brand range of classes including various animals and objects while CUB is a more fine-grained dataset that includes various species of birds. Datasets with different levels of granularity may have different distributions for their feature space. We want to show the effectiveness and generality of our strategy on all three datasets.
